Statistical learning reading seminar

Goal

Discuss machine learning techniques.

Schedule

Date Presenter Paper Comments
1: 27-10-2015 Alexander Ly Breiman, L (2001) and Ch 2 Slides
2: 17-11-2015 Tahira Jamil Linear regression (Ch 3) Slides
3: 01-12-2015 Lourens Waldorp Logistic Regression Slides
4: 12-01-2016 Johnny van Doorn, Quentin Gronau Resampling techniques Slides a, Slides b
5: 26-01-2016 Lourens Waldorp Regularisation: Ridge, lasso Slides
6: 09-02-2016 Lourens Waldorp Gaussian graphical models and the graphical lasso Slides
7: 23-02-2016 Alexander Ly Motivating splines Slides
8: 08-03-2016 Alexander Ly Smoothing splines Slides
9: 29-03-2016 Riet van Bork Classification and regression trees Slides
10: 05-04-2016 Joost Kruis Neural networks Slides
11: 19-04-2016 Jonas Haslbeck Bagging Slides
12: 10-05-2016 Gilles de Hollander Machine learning in cognitive neuroscience Slides
13: 31-05-2016 Udo Bohm Support vector machines Slides
14: 02-11-2016 Alexander Ly Recap Slides
15: 23-11-2016 Jonas Haslbeck Clustering Slides
16: 07-12-2016 Pia Tio Dimensionality reduction Slides
17: 25-01-2017 Gilles de Hollander Mixture modelling Slides
18: 15-02-2017 Don van den Bergh Recommender systems Slides
19: 01-03-2017 Johnny van Doorn PageRank
20: 22-03-2017 Koen Derks N-grams
21: 12-04-2017 Claire Stevenson Topic modelling
20: 26-04-2017 Quentin Gronau Topic modelling and Bayesian nonparametrics

Literature

James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R
Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction
– Breiman, L (2001). Statistical modeling The two cultures

Suggested reading

1: Alexander Ly – General overview

– Breiman, L (2001). Statistical modeling The two cultures”
– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 1, 2
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch1

2: Tahira Jamil – Linear regression

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 3
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction –

3: Lourens Waldorp – Logistic regression

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 4
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction –

4: Johnny van Doorn – K-fold cross validation and Quentin Gronau – Bootstrapping

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 5

5: Lourens Waldorp – Regularisation: Ridge, lasso

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 6

6: Lourens Waldorp – Gaussian graphical models and the graphical lasso

7: Alexander Ly – Motivating: Splines

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 7
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 5

9: Riet van Bork – Classification and regression trees

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 8
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 9

10: Joost Kruis – Neural networks

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 11

Jonas Haslbeck – Bagging

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 8.2

13: Udo Bohm- Suppor vector machines

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 9
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 12

15: Jonas Haslbeck – Clustering

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 10
– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Ch 14

16: Pia Tio – Dimensionality reduction

– James, G, Witten, D, Hastie, T, Tibshirani, R (2013). An introduction to statistical learning – with applications in R – Ch 10

17: Gilles de Hollander – Mixture modelling

– Hastie, T, Tibshirani, R, Friedman, JH (2001), The elements of statistical learning Data mining, inference, and prediction – Section 6.8, 8.5, 13.2.3, 14.3.7,
– Marin, JM, Robert, CP (2014). Bayesian essentials with R – Chapter 6
– Bishop, CM (2006). Pattern recognition and machine learning – Section 2.3.9, 5.6, Chapter 9
– Murphy, KP (2012). Machine learning – A probabilistic perspective – Chapter 11

18: Don van den Bergh – Recommender systems

– Ricci, F, Rokach, L, Shapira, B, Kantor, PB (2010). Recommender Systems Handbook – Ch1
– Gorakala, SK, Usuelli, M (2015). Building a Recommendation System with R- Learn the art of building robust and powerful recommendation engines using R
– https://www.coursera.org/learn/recommender-systems-introduction

19: Johnny van Doorn – PageRank

– Barber, D (2015). Bayesian reasoning and machine learning – Ch 23
– Murphy, KP (2012). Machine learning – A probabilistic perspective – Ch 17
– http://www.ams.org/samplings/feature-column/fcarc-pagerank
– Russell, S, Norvig, P (2009). Artificial intelligence – A modern approach – 3rd – Ch 22.3

20: Koen Derks – Text mining

– https://www.youtube.com/watch?t=25s&v=GTrkTDCyO80&app=desktop

20: Claire Stevenson – Topic modelling

20: Quentin Gronau – Topic modelling and Bayesian nonparametrics

Videos

Videos by Trevor Hastie and Rob Tibshirani
– MIT Opencourseware: Artificial Intelligence Videos by Patrick Henry Winston